SciFinder Scholar 2006: An empirical analysis of research topic query processing
SciFinder Scholar 2006: An empirical analysis of research topic query processing
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DOI：
10.1021/ci050481b
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发表时间：
2006-03-01
影响因子：
5.6
通讯作者：
Wagner, AB
中科院分区：
文献类型：
--
作者：
Wagner, AB
SciFinder Scholar中的主题搜索查询通过一套广泛的自然语言处理算法进行处理，大大提高了搜索结果的相关性和全面性。关于这些算法的详细文档很少发表。但是，仔细检查突出显示的热门词，并比较查询语言的微小变化的结果，就会发现关于这些算法的许多其他有用信息。了解这些算法的工作原理可以获得更好的搜索结果，并解释许多意想不到的结果，包括单数和复数查询单词和短语的不同命中数。
Topical search queries in SciFinder Scholar are processed through an extensive set of natural language processing algorithms that greatly enhance the relevance and comprehensiveness of the search results. Little detailed documentation on these algorithms has been published. However, a careful examination of the highlighted hit terms coupled with a comparison of results from small variations in query language reveal much additional, useful information about these algorithms. An understanding of how these algorithms work can lead to better search results and explain many unexpected results, including differing hit counts for singular versus plural query words and phrases.